A plug-in attribute correction module for generalized zero-shot learning.

• The first work that can correct attributes while preserving the original meaning of each attribute dimension. • The first plug-in module that aims to facilitate existing approaches and makes ZSL models eligible or even GZSL tasks. • The corrected attributes can better reflect the visual contexts without losing the integrability in attributes. While Zero Shot Learning models can recognize new classes without training examples, they often fails to incorporate both seen and unseen classes together at the test time, which is known as the Generalized Zero-shot Learning (GZSL) problem. This paper identifies a bottleneck issue when attributes are not well-defined, reliable, inaccurate in quantitative representations, or suffering from the visual-semantic discrepancy. We propose a Generic Plug-in Attribute Correction (GPAC) module which can effectively accommodate conventional ZSL in GZSL tasks. Different from existing embedding-based approaches which often lose the favor of transparency in attributes, our key challenge is to fully preserve the original meaning of the attributes and make it complementary and interpretable to upgrade existing ZSL models. To this end, we propose a novel nonnegative constraint with iterative Stochastic Gradient Descent toolbox to effectively fit our GPAC module into previous ZSL models. Extensive experiments on five popular datasets show that our method can effectively correct attributes and make conventional ZSL can achieve state-of-the-art performance on GZSL tasks. It is also a good practice for future models when incorporating prior human knowledge. [ABSTRACT FROM AUTHOR]